New York Times Comments

Dataset statistics

Number of variables	24
Number of observations	49865
Missing cells	106448
Missing cells (%)	8.9%
Duplicate rows	0
Duplicate rows (%)	0.0%
Total size in memory	8.5 MiB
Average record size in memory	178.0 B

Variable types

Numeric	8
Categorical	14
Boolean	2

Alerts

`status` has constant value "approved"	Constant
`trusted` has constant value "0"	Constant
`recommendedFlag` has constant value "0"	Constant
`isAnonymous` has constant value "False"	Constant
`userDisplayName` has a high cardinality: 20041 distinct values	High cardinality
`userLocation` has a high cardinality: 8257 distinct values	High cardinality
`commentBody` has a high cardinality: 49849 distinct values	High cardinality
`createDate` has a high cardinality: 49803 distinct values	High cardinality
`updateDate` has a high cardinality: 49793 distinct values	High cardinality
`approveDate` has a high cardinality: 49237 distinct values	High cardinality
`parentUserDisplayName` has a high cardinality: 10037 distinct values	High cardinality
`articleID` has a high cardinality: 9448 distinct values	High cardinality
`df_index` is highly correlated with `commentID` and 3 other fields	High correlation
`commentID` is highly correlated with `df_index` and 3 other fields	High correlation
`commentSequence` is highly correlated with `df_index` and 3 other fields	High correlation
`parentID` is highly correlated with `df_index` and 3 other fields	High correlation
`permID` is highly correlated with `df_index` and 3 other fields	High correlation
`df_index` is highly correlated with `commentID` and 3 other fields	High correlation
`commentID` is highly correlated with `df_index` and 3 other fields	High correlation
`commentSequence` is highly correlated with `df_index` and 3 other fields	High correlation
`recommendations` is highly correlated with `replyCount`	High correlation
`replyCount` is highly correlated with `recommendations`	High correlation
`parentID` is highly correlated with `df_index` and 3 other fields	High correlation
`permID` is highly correlated with `df_index` and 3 other fields	High correlation
`df_index` is highly correlated with `commentID` and 3 other fields	High correlation
`commentID` is highly correlated with `df_index` and 3 other fields	High correlation
`commentSequence` is highly correlated with `df_index` and 3 other fields	High correlation
`parentID` is highly correlated with `df_index` and 3 other fields	High correlation
`permID` is highly correlated with `df_index` and 3 other fields	High correlation
`editorsSelection` is highly correlated with `userTitle` and 4 other fields	High correlation
`commentType` is highly correlated with `userTitle` and 5 other fields	High correlation
`userTitle` is highly correlated with `editorsSelection` and 6 other fields	High correlation
`recommendedFlag` is highly correlated with `editorsSelection` and 6 other fields	High correlation
`trusted` is highly correlated with `editorsSelection` and 6 other fields	High correlation
`isAnonymous` is highly correlated with `editorsSelection` and 6 other fields	High correlation
`depth` is highly correlated with `commentType` and 5 other fields	High correlation
`status` is highly correlated with `editorsSelection` and 6 other fields	High correlation
`df_index` is highly correlated with `commentID` and 3 other fields	High correlation
`commentID` is highly correlated with `df_index` and 3 other fields	High correlation
`commentSequence` is highly correlated with `df_index` and 3 other fields	High correlation
`userID` is highly correlated with `userTitle`	High correlation
`userTitle` is highly correlated with `userID` and 1 other fields	High correlation
`recommendations` is highly correlated with `replyCount` and 1 other fields	High correlation
`replyCount` is highly correlated with `recommendations`	High correlation
`editorsSelection` is highly correlated with `recommendations`	High correlation
`parentID` is highly correlated with `df_index` and 3 other fields	High correlation
`depth` is highly correlated with `userTitle` and 1 other fields	High correlation
`commentType` is highly correlated with `depth`	High correlation
`permID` is highly correlated with `df_index` and 3 other fields	High correlation
`userTitle` has 49823 (99.9%) missing values	Missing
`parentID` has 28290 (56.7%) missing values	Missing
`parentUserDisplayName` has 28299 (56.8%) missing values	Missing
`recommendations` is highly skewed (γ1 = 20.49125599)	Skewed
`commentBody` is uniformly distributed	Uniform
`createDate` is uniformly distributed	Uniform
`updateDate` is uniformly distributed	Uniform
`approveDate` is uniformly distributed	Uniform
`df_index` has unique values	Unique
`commentID` has unique values	Unique
`commentSequence` has unique values	Unique
`permID` has unique values	Unique
`recommendations` has 7591 (15.2%) zeros	Zeros
`replyCount` has 42417 (85.1%) zeros	Zeros

Reproduction

Analysis started	2021-11-23 03:15:30.342270
Analysis finished	2021-11-23 03:16:11.422607
Duration	41.08 seconds
Software version	pandas-profiling v3.1.0
Download configuration	config.json

df_index
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct	49865
Distinct (%)	100.0%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	2488859.417

Minimum	80
Maximum	4986059
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	389.7 KiB

Quantile statistics

Minimum	80
5-th percentile	251669.8
Q1	1246092
median	2489626
Q3	3723073
95-th percentile	4733460.2
Maximum	4986059
Range	4985979
Interquartile range (IQR)	2476981

Descriptive statistics

Standard deviation	1435364.497
Coefficient of variation (CV)	0.5767157785
Kurtosis	-1.19129154
Mean	2488859.417
Median Absolute Deviation (MAD)	1238450
Skewness	0.003078892911
Sum	1.241069749 × 10¹¹
Variance	2.060271238 × 10¹²
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
2893132	1	< 0.1%
2237302	1	< 0.1%
3659185	1	< 0.1%
850669	1	< 0.1%
1110388	1	< 0.1%
2988085	1	< 0.1%
706265	1	< 0.1%
125380	1	< 0.1%
301951	1	< 0.1%
2982810	1	< 0.1%
Other values (49855)	49855	> 99.9%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
80	1	< 0.1%
131	1	< 0.1%
455	1	< 0.1%
500	1	< 0.1%
520	1	< 0.1%
701	1	< 0.1%
707	1	< 0.1%
720	1	< 0.1%
773	1	< 0.1%
786	1	< 0.1%

Value	Count	Frequency (%)
4986059	1	< 0.1%
4986058	1	< 0.1%
4985943	1	< 0.1%
4985892	1	< 0.1%
4985775	1	< 0.1%
4985595	1	< 0.1%
4985481	1	< 0.1%
4985440	1	< 0.1%
4985123	1	< 0.1%
4985082	1	< 0.1%

commentID
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct	49865
Distinct (%)	100.0%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	107599365

Minimum	104388915
Maximum	110891710
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	389.7 KiB

Quantile statistics

Minimum	104388915
5-th percentile	104723349.2
Q1	105992430
median	107593218
Q3	109183238
95-th percentile	110511574.2
Maximum	110891710
Range	6502795
Interquartile range (IQR)	3190808

Descriptive statistics

Standard deviation	1851390.306
Coefficient of variation (CV)	0.01720633116
Kurtosis	-1.187209893
Mean	107599365
Median Absolute Deviation (MAD)	1594642
Skewness	0.0169640153
Sum	5.365442333 × 10¹²
Variance	3.427646066 × 10¹²
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
108107200	1	< 0.1%
107262171	1	< 0.1%
109095286	1	< 0.1%
105473135	1	< 0.1%
105807973	1	< 0.1%
108220563	1	< 0.1%
105297582	1	< 0.1%
104559621	1	< 0.1%
104795341	1	< 0.1%
108213186	1	< 0.1%
Other values (49855)	49855	> 99.9%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
104388915	1	< 0.1%
104390966	1	< 0.1%
104394141	1	< 0.1%
104394616	1	< 0.1%
104394677	1	< 0.1%
104395348	1	< 0.1%
104395399	1	< 0.1%
104395730	1	< 0.1%
104395779	1	< 0.1%
104395866	1	< 0.1%

Value	Count	Frequency (%)
110891710	1	< 0.1%
110889621	1	< 0.1%
110886126	1	< 0.1%
110869755	1	< 0.1%
110863549	1	< 0.1%
110863371	1	< 0.1%
110862547	1	< 0.1%
110858823	1	< 0.1%
110857015	1	< 0.1%
110856939	1	< 0.1%

status
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct	1
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	389.7 KiB

approved	49865

Length

Max length	8
Median length	8
Mean length	8
Min length	8

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	approved
2nd row	approved
3rd row	approved
4th row	approved
5th row	approved

Common Values

Value	Count	Frequency (%)
approved	49865	100.0%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
approved	49865	100.0%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

commentSequence
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct	49865
Distinct (%)	100.0%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	107599365

Minimum	104388915
Maximum	110891710
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	389.7 KiB

Quantile statistics

Minimum	104388915
5-th percentile	104723349.2
Q1	105992430
median	107593218
Q3	109183238
95-th percentile	110511574.2
Maximum	110891710
Range	6502795
Interquartile range (IQR)	3190808

Descriptive statistics

Standard deviation	1851390.306
Coefficient of variation (CV)	0.01720633116
Kurtosis	-1.187209893
Mean	107599365
Median Absolute Deviation (MAD)	1594642
Skewness	0.0169640153
Sum	5.365442333 × 10¹²
Variance	3.427646066 × 10¹²
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
108107200	1	< 0.1%
107262171	1	< 0.1%
109095286	1	< 0.1%
105473135	1	< 0.1%
105807973	1	< 0.1%
108220563	1	< 0.1%
105297582	1	< 0.1%
104559621	1	< 0.1%
104795341	1	< 0.1%
108213186	1	< 0.1%
Other values (49855)	49855	> 99.9%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
104388915	1	< 0.1%
104390966	1	< 0.1%
104394141	1	< 0.1%
104394616	1	< 0.1%
104394677	1	< 0.1%
104395348	1	< 0.1%
104395399	1	< 0.1%
104395730	1	< 0.1%
104395779	1	< 0.1%
104395866	1	< 0.1%

Value	Count	Frequency (%)
110891710	1	< 0.1%
110889621	1	< 0.1%
110886126	1	< 0.1%
110869755	1	< 0.1%
110863549	1	< 0.1%
110863371	1	< 0.1%
110862547	1	< 0.1%
110858823	1	< 0.1%
110857015	1	< 0.1%
110856939	1	< 0.1%

userID
Real number (ℝ_≥0)

HIGH CORRELATION

Distinct	27077
Distinct (%)	54.3%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	59882166.51

Minimum	1166
Maximum	156554620
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	389.7 KiB

Quantile statistics

Minimum	1166
5-th percentile	6166787.8
Q1	44254092
median	63636343
Q3	76607983
95-th percentile	104582649.8
Maximum	156554620
Range	156553454
Interquartile range (IQR)	32353891

Descriptive statistics

Standard deviation	28824197.61
Coefficient of variation (CV)	0.4813486098
Kurtosis	0.365014948
Mean	59882166.51
Median Absolute Deviation (MAD)	15116816
Skewness	-0.002691459565
Sum	2.986024233 × 10¹²
Variance	8.308343676 × 10¹⁴
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
67892453	89	0.2%
66788641	74	0.1%
93082167	73	0.1%
68938663	68	0.1%
72967915	67	0.1%
63483890	60	0.1%
12840669	54	0.1%
58671272	51	0.1%
78358680	50	0.1%
51878992	45	0.1%
Other values (27067)	49234	98.7%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
1166	5	< 0.1%
2622	1	< 0.1%
4796	1	< 0.1%
6208	1	< 0.1%
7176	1	< 0.1%
8072	1	< 0.1%
8638	2	< 0.1%
8958	1	< 0.1%
9155	1	< 0.1%
11144	1	< 0.1%

Value	Count	Frequency (%)
156554620	1	< 0.1%
156454736	1	< 0.1%
156155108	1	< 0.1%
156143197	1	< 0.1%
156069975	2	< 0.1%
156047458	1	< 0.1%
156018626	1	< 0.1%
155912866	1	< 0.1%
155544210	1	< 0.1%
155352682	1	< 0.1%

userDisplayName
Categorical

HIGH CARDINALITY

Distinct	20041
Distinct (%)	40.2%
Missing	16
Missing (%)	< 0.1%
Memory size	389.7 KiB

John	267
David	255
Robert	182
Mark	179
Michael	177
Other values (20036)	48789

Length

Max length	203
Median length	7
Mean length	7.906698229
Min length	1

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	13413 ?
Unique (%)	26.9%

Sample

1st row	kdw
2nd row	Michael
3rd row	Tsumomi
4th row	FilmFan
5th row	Andie

Common Values

Value	Count	Frequency (%)
John	267	0.5%
David	255	0.5%
Robert	182	0.4%
Mark	179	0.4%
Michael	177	0.4%
Steve	176	0.4%
Mike	163	0.3%
Tom	144	0.3%
Paul	144	0.3%
Chris	142	0.3%
Other values (20031)	48020	96.3%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
john	789	1.1%
david	619	0.9%
steve	473	0.7%
robert	438	0.6%
michael	433	0.6%
mark	409	0.6%
b	409	0.6%
m	408	0.6%
paul	353	0.5%
s	336	0.5%
Other values (16243)	66017	93.4%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

userLocation
Categorical

HIGH CARDINALITY

Distinct	8257
Distinct (%)	16.6%
Missing	20
Missing (%)	< 0.1%
Memory size	389.7 KiB

NYC	1680
New York	1188
California	1040
USA	694
NY	623
Other values (8252)	44620

Length

Max length	49
Median length	9
Mean length	9.324365533
Min length	1

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	4621 ?
Unique (%)	9.3%

Sample

1st row	Louisville, KY
2nd row	Ottawa
3rd row	Portland, OR
4th row	Y’allywood
5th row	Geneva, NY

Common Values

Value	Count	Frequency (%)
NYC	1680	3.4%
New York	1188	2.4%
California	1040	2.1%
USA	694	1.4%
NY	623	1.2%
Seattle	621	1.2%
Chicago	615	1.2%
Boston	573	1.1%
San Francisco	554	1.1%
Brooklyn	553	1.1%
Other values (8247)	41704	83.6%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
new	3240	4.0%
ny	2729	3.4%
york	2284	2.9%
nyc	2048	2.6%
ca	1958	2.4%
california	1364	1.7%
san	1309	1.6%
nj	1109	1.4%
usa	923	1.2%
chicago	840	1.0%
Other values (4550)	62317	77.8%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

userTitle
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct	12
Distinct (%)	28.6%
Missing	49823
Missing (%)	99.9%
Memory size	389.7 KiB

The New York Times	14
editor, Wordplay	10
Reporter	6
Columnist	3
Editorial board writer	2
Other values (7)	7

Length

Max length	34
Median length	16
Mean length	16
Min length	6

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	7 ?
Unique (%)	16.7%

Sample

1st row	The New York Times
2nd row	Assistant Editor, Interactive News
3rd row	The New York Times
4th row	Reporter
5th row	Reporter

Common Values

Value	Count	Frequency (%)
The New York Times	14	< 0.1%
editor, Wordplay	10	< 0.1%
Reporter	6	< 0.1%
Columnist	3	< 0.1%
Editorial board writer	2	< 0.1%
Assistant Editor, Interactive News	1	< 0.1%
Science reporter	1	< 0.1%
Contributor, former Times reporter	1	< 0.1%
Foreign Correspondent	1	< 0.1%
Editor	1	< 0.1%
Other values (2)	2	< 0.1%
(Missing)	49823	99.9%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
times	15	13.9%
the	14	13.0%
york	14	13.0%
new	14	13.0%
editor	13	12.0%
wordplay	10	9.3%
reporter	8	7.4%
columnist	3	2.8%
editorial	2	1.9%
board	2	1.9%
Other values (12)	13	12.0%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

commentBody
Categorical

HIGH CARDINALITY
UNIFORM

Distinct	49849
Distinct (%)	> 99.9%
Missing	0
Missing (%)	0.0%
Memory size	389.7 KiB

Thank you.	4
Well said.	3
Brilliant!	3
Agreed.	2
No.	2
Other values (49844)	49851

Length

Max length	2592
Median length	282
Mean length	388.06666
Min length	3

Characters and Unicode

Total characters	82661
Distinct characters	1
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	49837 ?
Unique (%)	99.9%

Sample

1st row	@Jo Williams Exactly I see a nightmarish Gotham City for all of USA with no Batman or superhero to help save us.
2nd row	@Bugmon Are you really that obtuse? I do wear a mask when I go out. My point is that wearing a mask is not a 100% guarantee against spreading infection.
3rd row	It's so sad to see Trump's impact on our nation. This week's war he tried to start, rolling back environmental laws, and opening up national parks to oil exploration, the impeachable offenses he and his criminal friends committed, and all his continuous bluffing and luing. It's almost too much to listen to or watch or read. I agree with the article, he is bringing down the nation to his level, which is mentally ill, and certainly not very smart. I looked up Vegas bets tonight about his probability of winning the next election, and they all seem to think he's the man. Tragic for our nation if it turns out to be true. I so wish our nation was on a brighter path.
4th row	The children’s hospital in my city has enormous cash reserves and is constantly building urgent care centers, new hospital wings, etc. I realize it is unpopular to criticize a children’s hospital but I find it utterly ridiculous that so many women in my community donate untold thousands of hours hosting charity events and decorating mailbox wreaths to support this hospital that sits on millions in cash. Do they realize this money is not going directly to children and doctors and nurses but likely towards executive salaries for even more fundraising and buildings? Friends who work at the hospital in management have told me they are unwilling to work with or share any resources with other hospitals. Toxic fiefdoms and culture are rampant. Healthcare is a greedy business even for children and it needs to change.
5th row	I can't wait to watch this virtually with other folks. My grandmother introduced me to Cary Grant's films when I was just a young girl and I soon landed on this classic as my favorite. Rosalind Russell is a joy to watch and certainly is in the top 3 of Grant's co-stars in keeping up with him physically and verbally. If this movie isn't in Aaron Sorkin's cinematic DNA, I'd be shocked. Happy viewing!

Common Values

Value	Count	Frequency (%)
Thank you.	4	< 0.1%
Well said.	3	< 0.1%
Brilliant!	3	< 0.1%
Agreed.	2	< 0.1%
No.	2	< 0.1%
Excellent!	2	< 0.1%
Me too.	2	< 0.1%
Amen.	2	< 0.1%
YES!	2	< 0.1%
Agreed	2	< 0.1%
Other values (49839)	49841	> 99.9%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
the	167084	5.0%
to	97261	2.9%
and	89968	2.7%
of	76870	2.3%
a	71414	2.1%
is	54279	1.6%
in	52772	1.6%
that	46687	1.4%
i	37614	1.1%
for	34682	1.0%
Other values (82528)	2598680	78.1%

Most occurring characters

Value	Count	Frequency (%)
	82661	100.0%

Most occurring categories

Value	Count	Frequency (%)
Control	82661	100.0%

Most frequent character per category

Control

Value	Count	Frequency (%)
	82661	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Common	82661	100.0%

Most frequent character per script

Common

Value	Count	Frequency (%)
	82661	100.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	82661	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
	82661	100.0%

createDate
Categorical

HIGH CARDINALITY
UNIFORM

Distinct	49803
Distinct (%)	99.9%
Missing	0
Missing (%)	0.0%
Memory size	389.7 KiB

2020-02-16 16:51:10	2
2020-09-22 11:21:05	2
2020-09-09 21:09:28	2
2020-11-13 17:25:46	2
2020-07-14 15:25:37	2
Other values (49798)	49855

Length

Max length	19
Median length	19
Mean length	19
Min length	19

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	49741 ?
Unique (%)	99.8%

Sample

1st row	2020-07-13 15:33:55
2nd row	2020-06-23 01:09:24
3rd row	2020-01-10 06:21:20
4th row	2020-06-08 15:03:28
5th row	2020-04-03 15:25:21

Common Values

Value	Count	Frequency (%)
2020-02-16 16:51:10	2	< 0.1%
2020-09-22 11:21:05	2	< 0.1%
2020-09-09 21:09:28	2	< 0.1%
2020-11-13 17:25:46	2	< 0.1%
2020-07-14 15:25:37	2	< 0.1%
2020-05-29 12:56:55	2	< 0.1%
2020-06-10 15:42:53	2	< 0.1%
2020-12-12 04:12:53	2	< 0.1%
2020-01-02 14:20:40	2	< 0.1%
2020-10-23 13:59:27	2	< 0.1%
Other values (49793)	49845	> 99.9%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
2020-03-04	248	0.2%
2020-03-24	246	0.2%
2020-07-08	240	0.2%
2020-02-12	237	0.2%
2020-02-05	236	0.2%
2020-03-11	231	0.2%
2020-07-07	230	0.2%
2020-04-08	230	0.2%
2020-03-05	228	0.2%
2020-04-01	226	0.2%
Other values (35797)	97378	97.6%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

updateDate
Categorical

HIGH CARDINALITY
UNIFORM

Distinct	49793
Distinct (%)	99.9%
Missing	0
Missing (%)	0.0%
Memory size	389.7 KiB

2020-10-28 15:09:11	2
2020-04-02 15:12:57	2
2020-04-03 01:47:20	2
2020-08-04 13:28:14	2
2020-06-18 23:04:34	2
Other values (49788)	49855

Length

Max length	19
Median length	19
Mean length	19
Min length	19

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	49721 ?
Unique (%)	99.7%

Sample

1st row	2020-07-14 16:47:04
2nd row	2020-06-25 10:27:26
3rd row	2020-01-11 20:01:58
4th row	2020-06-09 15:40:03
5th row	2020-04-05 04:31:06

Common Values

Value	Count	Frequency (%)
2020-10-28 15:09:11	2	< 0.1%
2020-04-02 15:12:57	2	< 0.1%
2020-04-03 01:47:20	2	< 0.1%
2020-08-04 13:28:14	2	< 0.1%
2020-06-18 23:04:34	2	< 0.1%
2020-09-16 05:13:59	2	< 0.1%
2020-06-08 04:44:11	2	< 0.1%
2020-07-29 13:27:56	2	< 0.1%
2020-03-11 17:36:58	2	< 0.1%
2020-04-26 20:43:10	2	< 0.1%
Other values (49783)	49845	> 99.9%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
2020-06-02	219	0.2%
2020-03-11	214	0.2%
2020-02-13	211	0.2%
2020-02-05	210	0.2%
2020-07-07	206	0.2%
2020-02-06	205	0.2%
2020-02-12	204	0.2%
2020-04-16	202	0.2%
2020-03-04	200	0.2%
2020-07-08	198	0.2%
Other values (36728)	97661	97.9%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

approveDate
Categorical

HIGH CARDINALITY
UNIFORM

Distinct	49237
Distinct (%)	98.7%
Missing	0
Missing (%)	0.0%
Memory size	389.7 KiB

2020-04-09 15:39:56	3
2020-09-19 19:49:17	3
2020-03-01 18:30:44	3
2021-01-01 01:15:49	3
2020-04-21 11:54:57	3
Other values (49232)	49850

Length

Max length	19
Median length	19
Mean length	19
Min length	19

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	48616 ?
Unique (%)	97.5%

Sample

1st row	2020-07-13 16:11:46
2nd row	2020-06-23 01:27:44
3rd row	2020-01-10 06:21:22
4th row	2020-06-08 15:03:29
5th row	2020-04-03 15:25:22

Common Values

Value	Count	Frequency (%)
2020-04-09 15:39:56	3	< 0.1%
2020-09-19 19:49:17	3	< 0.1%
2020-03-01 18:30:44	3	< 0.1%
2021-01-01 01:15:49	3	< 0.1%
2020-04-21 11:54:57	3	< 0.1%
2020-03-26 20:25:32	3	< 0.1%
2020-04-09 15:32:07	3	< 0.1%
2020-02-01 15:47:38	2	< 0.1%
2020-03-05 13:12:01	2	< 0.1%
2020-02-27 04:11:22	2	< 0.1%
Other values (49227)	49838	99.9%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
2020-02-05	246	0.2%
2020-03-04	234	0.2%
2020-02-12	234	0.2%
2020-03-24	233	0.2%
2020-07-07	232	0.2%
2020-06-01	229	0.2%
2020-03-11	228	0.2%
2020-02-13	226	0.2%
2020-03-05	225	0.2%
2020-07-08	222	0.2%
Other values (34460)	97421	97.7%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

recommendations
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct	673
Distinct (%)	1.3%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	20.60366991

Minimum	0
Maximum	5238
Zeros	7591
Zeros (%)	15.2%
Negative	0
Negative (%)	0.0%
Memory size	389.7 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	1
median	4
Q3	12
95-th percentile	62
Maximum	5238
Range	5238
Interquartile range (IQR)	11

Descriptive statistics

Standard deviation	108.7872359
Coefficient of variation (CV)	5.279993145
Kurtosis	623.3599352
Mean	20.60366991
Median Absolute Deviation (MAD)	4
Skewness	20.49125599
Sum	1027402
Variance	11834.66269
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0	7591	15.2%
1	6144	12.3%
2	4809	9.6%
3	3925	7.9%
4	3180	6.4%
5	2473	5.0%
6	2115	4.2%
7	1734	3.5%
8	1462	2.9%
9	1298	2.6%
Other values (663)	15134	30.3%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	7591	15.2%
1	6144	12.3%
2	4809	9.6%
3	3925	7.9%
4	3180	6.4%
5	2473	5.0%
6	2115	4.2%
7	1734	3.5%
8	1462	2.9%
9	1298	2.6%

Value	Count	Frequency (%)
5238	1	< 0.1%
5153	1	< 0.1%
4805	1	< 0.1%
4348	1	< 0.1%
3804	1	< 0.1%
3788	1	< 0.1%
3613	1	< 0.1%
3514	1	< 0.1%
3505	1	< 0.1%
3396	1	< 0.1%

replyCount
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct	61
Distinct (%)	0.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	0.4413315953

Minimum	0
Maximum	117
Zeros	42417
Zeros (%)	85.1%
Negative	0
Negative (%)	0.0%
Memory size	389.7 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	0
Q3	0
95-th percentile	2
Maximum	117
Range	117
Interquartile range (IQR)	0

Descriptive statistics

Standard deviation	2.28637511
Coefficient of variation (CV)	5.180628656
Kurtosis	519.2815059
Mean	0.4413315953
Median Absolute Deviation (MAD)	0
Skewness	17.7089783
Sum	22007
Variance	5.227511142
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0	42417	85.1%
1	3737	7.5%
2	1542	3.1%
3	724	1.5%
4	421	0.8%
5	260	0.5%
6	165	0.3%
7	109	0.2%
8	90	0.2%
9	55	0.1%
Other values (51)	345	0.7%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	42417	85.1%
1	3737	7.5%
2	1542	3.1%
3	724	1.5%
4	421	0.8%
5	260	0.5%
6	165	0.3%
7	109	0.2%
8	90	0.2%
9	55	0.1%

Value	Count	Frequency (%)
117	1	< 0.1%
111	1	< 0.1%
89	1	< 0.1%
78	1	< 0.1%
70	1	< 0.1%
67	1	< 0.1%
63	1	< 0.1%
61	2	< 0.1%
60	2	< 0.1%
57	1	< 0.1%

editorsSelection
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct	2
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	48.8 KiB

False	49267
True	598

Common Values
Chart

Value	Count	Frequency (%)
False	49267	98.8%
True	598	1.2%

parentID
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct	21150
Distinct (%)	98.0%
Missing	28290
Missing (%)	56.7%
Infinite	0
Infinite (%)	0.0%
Mean	107552394

Minimum	104390618
Maximum	110879849
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	389.7 KiB

Quantile statistics

Minimum	104390618
5-th percentile	104728508.1
Q1	105936971.5
median	107514634
Q3	109100114
95-th percentile	110497977.2
Maximum	110879849
Range	6489231
Interquartile range (IQR)	3163142.5

Descriptive statistics

Standard deviation	1842000.367
Coefficient of variation (CV)	0.0171265399
Kurtosis	-1.178731317
Mean	107552394
Median Absolute Deviation (MAD)	1580151
Skewness	0.05460173987
Sum	2.3204429 × 10¹²
Variance	3.392965351 × 10¹²
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
104678200	4	< 0.1%
108251708	3	< 0.1%
107715231	3	< 0.1%
105656375	3	< 0.1%
107752488	3	< 0.1%
110385287	3	< 0.1%
109411598	3	< 0.1%
109385955	3	< 0.1%
108438132	3	< 0.1%
108406415	3	< 0.1%
Other values (21140)	21544	43.2%
(Missing)	28290	56.7%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
104390618	1	< 0.1%
104393988	1	< 0.1%
104393990	1	< 0.1%
104394007	1	< 0.1%
104394268	2	< 0.1%
104396158	1	< 0.1%
104396290	1	< 0.1%
104396379	1	< 0.1%
104396599	1	< 0.1%
104396761	1	< 0.1%

Value	Count	Frequency (%)
110879849	1	< 0.1%
110879196	1	< 0.1%
110858660	1	< 0.1%
110855296	1	< 0.1%
110850770	1	< 0.1%
110848112	1	< 0.1%
110842245	1	< 0.1%
110840719	1	< 0.1%
110839626	1	< 0.1%
110839242	1	< 0.1%

parentUserDisplayName
Categorical

HIGH CARDINALITY
MISSING

Distinct	10037
Distinct (%)	46.5%
Missing	28299
Missing (%)	56.8%
Memory size	389.7 KiB

John	147
Doug	147
David	117
Mark	108
Mike	108
Other values (10032)	20939

Length

Max length	48
Median length	7
Mean length	7.846703144
Min length	1

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	6943 ?
Unique (%)	32.2%

Sample

1st row	Jo Williams
2nd row	MIKEinNYC
3rd row	stan continople
4th row	EGD
5th row	Tom B.

Common Values

Value	Count	Frequency (%)
John	147	0.3%
Doug	147	0.3%
David	117	0.2%
Mark	108	0.2%
Mike	108	0.2%
Paul	92	0.2%
Michael	89	0.2%
Socrates	85	0.2%
Chris	78	0.2%
Steve	67	0.1%
Other values (10027)	20528	41.2%
(Missing)	28299	56.8%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
john	399	1.3%
david	303	1.0%
mark	231	0.8%
michael	209	0.7%
b	199	0.6%
m	196	0.6%
paul	192	0.6%
mike	187	0.6%
doug	174	0.6%
j	172	0.6%
Other values (8742)	28396	92.6%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

depth
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct	2
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	389.7 KiB

1	28290
2	21575

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	2
2nd row	2
3rd row	1
4th row	1
5th row	1

Common Values

Value	Count	Frequency (%)
1	28290	56.7%
2	21575	43.3%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
1	28290	56.7%
2	21575	43.3%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

commentType
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct	3
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	389.7 KiB

comment	28289
userReply	21534
reporterReply	42

Length

Max length	13
Median length	7
Mean length	7.868745613
Min length	7

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	userReply
2nd row	userReply
3rd row	comment
4th row	comment
5th row	comment

Common Values

Value	Count	Frequency (%)
comment	28289	56.7%
userReply	21534	43.2%
reporterReply	42	0.1%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
comment	28289	56.7%
userreply	21534	43.2%
reporterreply	42	0.1%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

trusted
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct	1
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	389.7 KiB

0	49865

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0
2nd row	0
3rd row	0
4th row	0
5th row	0

Common Values

Value	Count	Frequency (%)
0	49865	100.0%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
0	49865	100.0%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

recommendedFlag
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct	1
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	389.7 KiB

0	49865

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0
2nd row	0
3rd row	0
4th row	0
5th row	0

Common Values

Value	Count	Frequency (%)
0	49865	100.0%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
0	49865	100.0%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

permID
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct	49865
Distinct (%)	100.0%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	107599365

Minimum	104388915
Maximum	110891710
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	389.7 KiB

Quantile statistics

Minimum	104388915
5-th percentile	104723349.2
Q1	105992430
median	107593218
Q3	109183238
95-th percentile	110511574.2
Maximum	110891710
Range	6502795
Interquartile range (IQR)	3190808

Descriptive statistics

Standard deviation	1851390.306
Coefficient of variation (CV)	0.01720633116
Kurtosis	-1.187209893
Mean	107599365
Median Absolute Deviation (MAD)	1594642
Skewness	0.0169640153
Sum	5.365442333 × 10¹²
Variance	3.427646066 × 10¹²
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
108107200	1	< 0.1%
107262171	1	< 0.1%
109095286	1	< 0.1%
105473135	1	< 0.1%
105807973	1	< 0.1%
108220563	1	< 0.1%
105297582	1	< 0.1%
104559621	1	< 0.1%
104795341	1	< 0.1%
108213186	1	< 0.1%
Other values (49855)	49855	> 99.9%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
104388915	1	< 0.1%
104390966	1	< 0.1%
104394141	1	< 0.1%
104394616	1	< 0.1%
104394677	1	< 0.1%
104395348	1	< 0.1%
104395399	1	< 0.1%
104395730	1	< 0.1%
104395779	1	< 0.1%
104395866	1	< 0.1%

Value	Count	Frequency (%)
110891710	1	< 0.1%
110889621	1	< 0.1%
110886126	1	< 0.1%
110869755	1	< 0.1%
110863549	1	< 0.1%
110863371	1	< 0.1%
110862547	1	< 0.1%
110858823	1	< 0.1%
110857015	1	< 0.1%
110856939	1	< 0.1%

isAnonymous
Boolean

CONSTANT
HIGH CORRELATION
REJECTED

Distinct	1
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	48.8 KiB

False	49865

Common Values
Chart

Value	Count	Frequency (%)
False	49865	100.0%

articleID
Categorical

HIGH CARDINALITY

Distinct	9448
Distinct (%)	18.9%
Missing	0
Missing (%)	0.0%
Memory size	389.7 KiB

nyt://interactive/a36f9b6d-eea3-5c5c-be2d-ee90a2637d75	87
nyt://article/57736deb-c3d4-5640-8732-bce6442e16c4	70
nyt://article/b316ddae-bf03-5de8-b9e6-a796d0471430	52
nyt://article/b9c6769e-e64b-5c2c-8be1-6352c3bb6520	50
nyt://article/05e72c89-8cfb-5bd4-af50-5c429f9e05c7	48
Other values (9443)	49558

Length

Max length	54
Median length	50
Mean length	50.09714228
Min length	50

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	2800 ?
Unique (%)	5.6%

Sample

1st row	nyt://article/ceee4614-2a34-5964-afa8-e6c1aa2ce993
2nd row	nyt://article/5d10b98f-b66f-5bfc-9bd2-321abe8b8f3d
3rd row	nyt://article/1e5a3172-e5ed-57ae-824e-a8d6c974b696
4th row	nyt://article/8fc3fae7-64dc-5717-a2e7-0ad507dce6aa
5th row	nyt://article/dd05a4be-2e90-5fc2-ab6d-1423b8d9188a

Common Values

Value	Count	Frequency (%)
nyt://interactive/a36f9b6d-eea3-5c5c-be2d-ee90a2637d75	87	0.2%
nyt://article/57736deb-c3d4-5640-8732-bce6442e16c4	70	0.1%
nyt://article/b316ddae-bf03-5de8-b9e6-a796d0471430	52	0.1%
nyt://article/b9c6769e-e64b-5c2c-8be1-6352c3bb6520	50	0.1%
nyt://article/05e72c89-8cfb-5bd4-af50-5c429f9e05c7	48	0.1%
nyt://article/d35b554e-eda8-505f-80b0-3e9b9344ecd1	48	0.1%
nyt://article/de217dd9-3383-574a-9eaf-c0303570e794	47	0.1%
nyt://article/032c8257-cfe7-5e69-8042-4bfe2b13186f	47	0.1%
nyt://article/0583f62d-570d-50fd-acbd-7d0b78c50aaf	46	0.1%
nyt://article/abd7cf55-3363-5c57-9f25-734d72ce6530	45	0.1%
Other values (9438)	49325	98.9%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
nyt://interactive/a36f9b6d-eea3-5c5c-be2d-ee90a2637d75	87	0.2%
nyt://article/57736deb-c3d4-5640-8732-bce6442e16c4	70	0.1%
nyt://article/b316ddae-bf03-5de8-b9e6-a796d0471430	52	0.1%
nyt://article/b9c6769e-e64b-5c2c-8be1-6352c3bb6520	50	0.1%
nyt://article/05e72c89-8cfb-5bd4-af50-5c429f9e05c7	48	0.1%
nyt://article/d35b554e-eda8-505f-80b0-3e9b9344ecd1	48	0.1%
nyt://article/de217dd9-3383-574a-9eaf-c0303570e794	47	0.1%
nyt://article/032c8257-cfe7-5e69-8042-4bfe2b13186f	47	0.1%
nyt://article/0583f62d-570d-50fd-acbd-7d0b78c50aaf	46	0.1%
nyt://article/0525de83-8774-5f76-8d16-5b8e08a71b92	45	0.1%
Other values (9438)	49325	98.9%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

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Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

A simple visualization of nullity by column.

Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

First rows

	df_index	commentID	status	commentSequence	userID	userDisplayName	userLocation	userTitle	commentBody	createDate	updateDate	approveDate	recommendations	replyCount	editorsSelection	parentID	parentUserDisplayName	depth	commentType	permID	isAnonymous	articleID
0	2893132	108107200	approved	108107200	22178100	kdw	Louisville, KY	NaN	@Jo Williams Exactly I see a nightmarish Gotham City for all of USA with no Batman or superhero to help save us.	2020-07-13 15:33:55	2020-07-14 16:47:04	2020-07-13 16:11:46	2	0	False	108103586.0	Jo Williams	2	userReply	108107200	False	nyt://article/ceee4614-2a34-5964-afa8-e6c1aa2ce993
1	2599853	107731134	approved	107731134	55663429	Michael	Ottawa	NaN	@Bugmon \n\nAre you really that obtuse?\n\nI do wear a mask when I go out.\n\nMy point is that wearing a mask is not a 100% guarantee against spreading infection.	2020-06-23 01:09:24	2020-06-25 10:27:26	2020-06-23 01:27:44	1	0	False	107730611.0	MIKEinNYC	2	userReply	107731134	False	nyt://article/5d10b98f-b66f-5bfc-9bd2-321abe8b8f3d
2	125068	104552271	approved	104552271	61505299	Tsumomi	Portland, OR	NaN	It's so sad to see Trump's impact on our nation. This week's war he tried to start, rolling back environmental laws, and opening up national parks to oil exploration, the impeachable offenses he and his criminal friends committed, and all his continuous bluffing and luing. It's almost too much to listen to or watch or read. I agree with the article, he is bringing down the nation to his level, which is mentally ill, and certainly not very smart. I looked up Vegas bets tonight about his probability of winning the next election, and they all seem to think he's the man. Tragic for our nation if it turns out to be true. I so wish our nation was on a brighter path.	2020-01-10 06:21:20	2020-01-11 20:01:58	2020-01-10 06:21:22	44	1	False	NaN	NaN	1	comment	104552271	False	nyt://article/1e5a3172-e5ed-57ae-824e-a8d6c974b696
3	2425848	107493366	approved	107493366	55596960	FilmFan	Y’allywood	NaN	The children’s hospital in my city has enormous cash reserves and is constantly building urgent care centers, new hospital wings, etc. I realize it is unpopular to criticize a children’s hospital but I find it utterly ridiculous that so many women in my community donate untold thousands of hours hosting charity events and decorating mailbox wreaths to support this hospital that sits on millions in cash. Do they realize this money is not going directly to children and doctors and nurses but likely towards executive salaries for even more fundraising and buildings? Friends who work at the hospital in management have told me they are unwilling to work with or share any resources with other hospitals. Toxic fiefdoms and culture are rampant. Healthcare is a greedy business even for children and it needs to change.	2020-06-08 15:03:28	2020-06-09 15:40:03	2020-06-08 15:03:29	78	1	False	NaN	NaN	1	comment	107493366	False	nyt://article/8fc3fae7-64dc-5717-a2e7-0ad507dce6aa
4	1444275	106241790	approved	106241790	68597593	Andie	Geneva, NY	NaN	I can't wait to watch this virtually with other folks. My grandmother introduced me to Cary Grant's films when I was just a young girl and I soon landed on this classic as my favorite. Rosalind Russell is a joy to watch and certainly is in the top 3 of Grant's co-stars in keeping up with him physically and verbally. If this movie isn't in Aaron Sorkin's cinematic DNA, I'd be shocked. \nHappy viewing!	2020-04-03 15:25:21	2020-04-05 04:31:06	2020-04-03 15:25:22	8	1	False	NaN	NaN	1	comment	106241790	False	nyt://article/dd05a4be-2e90-5fc2-ab6d-1423b8d9188a
5	1737257	106608296	approved	106608296	42936585	Nancy G	MA	NaN	Doesn't he know the virus is already here??	2020-04-21 14:13:38	2020-04-21 15:05:32	2020-04-21 14:31:51	6	0	False	NaN	NaN	1	comment	106608296	False	nyt://article/3e52c8d2-b72d-5fd0-872d-d6b69e86affc
6	2803067	107989580	approved	107989580	107693923	bilbeo	new york, ny	NaN	@stan continople In Greenpoint as well and wondered the exact same thing as I wandered around Sunday to the waterfront and into Williamsburg. I felt so jealous for a moment to think about how good it would be to have that kind of money! These buildings are like nothing I have ever seen before. The area is alien to me; totally different from when I came here 13 years ago.	2020-07-07 14:38:52	2020-07-08 16:24:26	2020-07-07 14:38:53	17	0	False	107986742.0	stan continople	2	userReply	107989580	False	nyt://article/1eeac815-17ff-5493-967b-6eda132d5b00
7	3612604	109029854	approved	109029854	66072150	Johnny Panic	Burlington, Massachusetts	NaN	@EGD Oh boy, here we go again, that tired and well debunked John Birch Society originated "We are a constitutional republic, not a democracy" trope. It's a shame civics classes aren't widespread anymore, and it's sad when people who did take civics classes in school forget and actually argue against everything they were taught.	2020-09-09 13:50:33	2020-09-09 22:44:54	2020-09-09 14:14:23	2	0	False	109022963.0	EGD	2	userReply	109029854	False	nyt://article/25f302f9-34cb-5ea7-bdce-990af0303d2a
8	2906211	108124788	approved	108124788	97953061	Dar James	PA	NaN	Rugged Individualism.\n\nWhat would Jesus do?	2020-07-14 15:00:53	2020-07-14 17:41:58	2020-07-14 17:41:58	0	0	False	NaN	NaN	1	comment	108124788	False	nyt://article/df1f79af-d370-5432-a384-8e61094c2f0d
9	2532219	107641499	approved	107641499	98563280	Paul-A	St. Lawrence, NY	NaN	@Tom B. \nBesides the reasons other people have cited, there are other factors which could possibly be factors:\n\n- There are different strains of the virus. Some are more lethal than others.\n\n- There are other health factors which increase the death rates (e.g. living in polluted areas, other health conditions, etc.)\n\n- There's some evidence of correlation with factors such as blood type, racial background, etc. These might not prove to be direct correlations (and are not causations); but they could be part of the complicated picture.\n\nBut until more info is known, it appears that the time lag between diagnosis and death that many others have commented about is a plausible explanation of these early numbers.	2020-06-17 02:33:27	2020-06-17 05:43:57	2020-06-17 02:33:29	1	0	False	107639368.0	Tom B.	2	userReply	107641499	False	nyt://article/2ec11b93-6f51-5107-9a9c-526d2cb72ea4

Last rows

	df_index	commentID	status	commentSequence	userID	userDisplayName	userLocation	userTitle	commentBody	createDate	updateDate	approveDate	recommendations	replyCount	editorsSelection	parentID	parentUserDisplayName	depth	commentType	permID	isAnonymous	articleID
49855	2747271	107925354	approved	107925354	29823394	Bokmal	USA	NaN	@Grace Important points. I have asthma and masks excerbate it. And I have tried a range of masks. More enlightened countries, like Canada, do not require those with lung disease, to wear masks.	2020-07-02 17:38:19	2020-07-02 17:38:20	2020-07-02 17:38:20	0	0	False	107913416.0	Rith	2	userReply	107925354	False	nyt://article/b661b5b0-95b5-55d9-8be6-d2b91d11897a
49856	4921862	110753692	approved	110753692	71180810	Marcos Campos	New York	NaN	What we have had all along is a juvenile know-nothing mentally unstable misfit in the White House and a political party that has seen better days, having anchored their future to a fraud and a criminal.\n\nLoeffler and Purdue hitched their wagons to this madman and now he's thrown them and GOP leadership under the bus.	2020-12-24 15:01:58	2020-12-25 20:42:19	2020-12-24 15:56:31	28	0	False	NaN	NaN	1	comment	110753692	False	nyt://article/8ee8d140-27f2-57b4-a39a-1a4fec1ddc6e
49857	2206339	107218445	approved	107218445	53630318	Steve	Portland, ME	NaN	@richard wiesner \nBravo! I sincerely hope that parents come to realize that teaching is a very challenging job, and paying for quality teachers is worth every cent. Plus, I also hope that people come to realize that lowering class sizes will greatly improve educational experience as well as help contain the spread of disease in the future. Most importantly, I hope parents start listening to teachers, and not listen to politicians and businesspersons for insights about education.	2020-05-25 10:25:22	2020-08-12 18:25:08	2020-05-25 10:25:23	42	0	False	107217555.0	richard wiesner	2	userReply	107218445	False	nyt://article/26c2e73c-1bf5-500e-bf00-be76e69087b2
49858	901071	105558311	approved	105558311	48701991	heidiwriter	Pacific Palisades, CA	NaN	@Mary, except conviction. Oh, but wait, you don't seem to believe such a thing is possible...	2020-03-02 20:18:44	2020-03-02 21:53:00	2020-03-02 21:53:00	0	0	False	105556632.0	JH	2	userReply	105558311	False	nyt://article/b14a3c25-8975-54e2-9d0c-a47abb0fe56f
49859	2111635	107094087	approved	107094087	77112720	Roberta	Kansas City	NaN	@Devil's Advocate I'm a school psychologist with my training primarily in cognitive psychology. The "verbal gaffes" that Biden occasionally struggles with can most definitely a symptom of a mild speech impediment such as stuttering. \nI see this all the time with the kids I've worked with -- their verbal reasoning may show a deficit in that area, while their abilities are often above average in other cognitive areas, such as nonverbal reasoning and fluid reasoning, executive functioning, processing information speed, working memory, etc. I believe this to be the case with Joe Biden. \nTrump, on the hand ... well that's an entirely different story. Trump is a raving, unhinged lunatic who should not be anywhere near the nuclear codes.	2020-05-17 18:08:35	2020-05-20 23:55:14	2020-05-17 18:27:57	26	0	False	107091755.0	Glen	2	userReply	107094087	False	nyt://article/0457a10e-6ae9-584f-aa93-e49474764a7e
49860	3009694	108244250	approved	108244250	11967380	mutineer	Geneva, NY	NaN	Best last sentence in any column, ever.	2020-07-21 12:25:27	2020-07-21 21:28:09	2020-07-21 20:50:57	1	0	False	NaN	NaN	1	comment	108244250	False	nyt://article/d3913502-4cc0-54ce-8372-8e98105ba9fd
49861	592853	105155962	approved	105155962	80110445	pi	maine	NaN	Roger Stone, along with Paul Manafort and Lee Atwater transformed the once respected GOP into the Republican party of dirty tricks as we know it today. \n\nTheir co conspirator Newt Gingrich begat Mitch McConnell. Before there was lying Donald Trump, there were Tricky Dick Nixon, genial vicious fabulist Ronald Reagan, and cherry picking war mongering Bush, Cheney, and Rumsfeld. Lies and more lies. Given the imprimatur of gospel truth by the religious right.\n\nBefore Donald Trump pardoned military war criminals, Bill Barr was getting Reagan's Iran Contra operatives off the hook.\n\nIts not the 'deep state', its the old boys club. Its the same players - right down to Donald Trump's mentor Roy Cohn at the intersection of Joe McCarthy and the Mob - in the tragedy of America's downfall playing out center stage.	2020-02-11 20:54:33	2020-02-13 20:40:02	2020-02-11 23:33:52	138	1	False	NaN	NaN	1	comment	105155962	False	nyt://article/da5df871-c799-5619-b9a0-ab6e4c8a0533
49862	1359418	106116945	approved	106116945	59919476	CharlesM1950	Austin TX	NaN	No one individual tweets 798 times in a day. It’s either a bot or a team. In either case the intent is to leverage the pandemic for the purpose of creating division in our country. I smell a rat, a rat named Putin. I also smell a send rat named Trump because he doesn’t actively separate himself from this divisive propaganda. When the Trump Presidency ends I am certain we will find a trail of droppings that lead straight to the Kremlin.	2020-03-29 03:03:00	2020-03-30 14:09:44	2020-03-29 16:40:21	6	0	False	NaN	NaN	1	comment	106116945	False	nyt://article/bd4b1fad-9b89-5d1e-95ed-301406ea4b65
49863	4886496	110704822	approved	110704822	66864730	Al M	Norfolk Va	NaN	@Anthony \n\nOr the lives of journalists and civilians?	2020-12-21 14:48:16	2020-12-21 23:13:55	2020-12-21 17:52:02	1	0	False	110704720.0	Anthony	2	userReply	110704822	False	nyt://article/e4dcbe3f-3182-52f1-b1d2-fb946ef7d4cd
49864	463789	105027047	approved	105027047	98839260	Kopec	Glenbard West, Glen Ellyn, IL	NaN	I believe Senator Klobuchar’s act of sharing a homemade hot dish recipe was a smart move when it comes to connecting with voters. Food is the ultimate connector, everyone needs food to survive and grow; and the act of eating food releases happy chemicals into our brains. Not only did Ms. Klobuchar share a friendly, open side of herself she has shown her commonalities with regular people. When one would regularly imagine a president the image of that president laboring away in the kitchen on a modest meal is not one the usually comes to mind. In many ways this act was “thinking outside the box” in a way that expressed senator Klobuchar true self to those who are going to be deciding the next leader of the free world. So in the age of increasing “fake ness” and media projections becoming reality I applaud a very real lady.	2020-02-05 16:40:11	2020-02-05 16:40:30	2020-02-05 16:40:30	0	0	False	NaN	NaN	1	comment	105027047	False	nyt://article/725a382f-a86d-5af3-8899-acd4e805487a

Overview

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ASCII

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